Version 3 with 517M hashes and counts of password usage ordered by most to least prevalent Pwned Passwords are 517,238,891 real world passwords previously exposed in data breaches. This exposure makes them unsuitable for ongoing use as they re at much greater risk of being used to take over other accounts. They re searchable online below as well as being downloadable for use in other online system. The entire set of passwords is downloadable for free below with each password being represented as a SHA-1 hash to protect the original value (some passwords contain personally identifiable information) followed by a count of how many times that password had been seen in the source data breaches. The list may be integrated into other systems and used to verify whether a password has previously appeared in a data breach after which a system may warn the user or even block the password outright.

The list contains every wordlist, dictionary, and password database leak that I could find on the internet (and I spent a LOT of time looking). It also contains every word in the Wikipedia databases (pages-articles, retrieved 2010, all languages) as well as lots of books from Project Gutenberg. It also includes the passwords from some low-profile database breaches that were being sold in the underground years ago. The format of the list is a standard text file sorted in non-case-sensitive alphabetical order. Lines are separated with a newline " " character. You can test the list without downloading it by giving SHA256 hashes to the free hash cracker or to @PlzCrack on twitter. Here s a tool for computing hashes easily. Here are the results of cracking LinkedIn s and eHarmony s password hash leaks with the list. The list is responsible for cracking about 30% of all hashes given to CrackStation s free hash cracker, but that figure should be taken with a grain of salt because s

Breaches

## Overview

Breaches is a dataset for object detection tasks - it contains Objects annotations for 265 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Between November 2022 and October 2023, 67 percent of compromised information in the healthcare industry was personal data. Furthermore, 60 percent of data compromised in the manufacturing industry was personal information, while 38 percent were compromised credentials.

Introduction This dataset records all curtailment events experienced by curtailable-connection customers. About Curtailment When a generation customer requests a firm connection under a congested part of our network, there may be a requirement to reinforce the network to accommodate the connection. The reinforcement works take time to complete which increases the lead time to connect for the customer. Furthermore, the customer may need to contribute to the cost of the reinforcement works.UK Power Networks offers curtailable-connections as an alternative solution for our customers. It allows customers to connect to the distribution network as soon as possible rather than waiting, and potentially paying, for network reinforcement. This is possible because under a curtailable connection, the customer agrees that their access to the network can be controlled when congestion is high. These fast-tracked curtailable-connections can transition to firm connections once the reinforcement activity has taken place. Curtailable connections have enabled faster and cheaper connection of renewable energy generation to the distribution network owned and operated by UK Power Networks.The Distribution System Operator (DSO) team has developed the Distributed Energy Resource Management System (DERMS) that monitors curtailable-connection generators as well as associated constraints on the network. When a constraint reaches a critical threshold, an export access reduction signal may be sent to generators associated with that constraint so that the network can be kept safe, secure, and reliable.This dataset contains a record of curtailment actions we have taken and the resultant access reduction experienced by our curtailment-connections customers. Access reduction is calculated as the MW access reduction from maximum × duration of access reduction in hours (MW×h). The dataset categorises curtailment actions into 2 categories: Constraint-driven curtailment: when a constraint is breached, we aggregate the access reduction of all customers associated with that constraint. A constraint breach occurs when the network load exceeds the safe limit. Non-constraint driven curtailment: this covers all curtailment which is not directly related to a constraint breach on the network. It includes customer comms failures, non-compliance trips (where the customer has not complied with a curtailment instruction), planned outages and unplanned outages Each row in the dataset details the start and end times, durations and customer access reduction associated with a curtailment actions. We also provide the associated grid supply point (GSP) and nominal voltage to provide greater aggregation capabilities. By virtue of being able to track curtailment across our network in granular detail, we have managed to significantly reduce curtailment of our curtailable-connections customers. Methodological Approach A Remote Terminal Unit (RTU) is installed at each curtailable-connection site providing live telemetry data into the DERMS. It measures communications status, generator output and mode of operation. RTUs are also installed at constraint locations (physical parts of the network, e.g., transformers, cables which may become overloaded under certain conditions). These are identified through planning power load studies. These RTUs monitor current at the constraint and communications status. The DERMS design integrates network topology information. This maps constraints to associated curtailable connections under different network running conditions, including the sensitivity of the constraints to each curtailable connection. In general, a 1MW reduction in generation of a customer will cause <1MW reduction at the constraint. Each constraint is registered to a GSP.DERMS monitors constraints against the associated breach limit. When a constraint limit is breached, DERMS calculates the amount of access reduction required from curtailable connections linked to the constraint to alleviate the breach. This calculation factors in the real-time level of generation of each customer and the sensitivity of the constraint to each generator. Access reduction is issued to each curtailable-connection via the RTU until the constraint limit breach is mitigated. Multiple constraints can apply to a curtailable-connection and constraint breaches can occur simultaneously. Where multiple constraint breaches act upon a single curtailable-connection, we apportion the access reduction of that connection to the constraint breaches depending on the relative magnitude of the breaches. Where customer curtailment occurs without any associated constraint breach, we categorise the curtailment as non-constraint driven. Future developments will include the reason for non-constraint driven curtailment. Quality Control Statement The dataset is derived from data recorded by RTUs located at customer sites and constraint locations across our network. UKPN’s Ops Telecoms team monitors and maintains these RTUs to ensure they are providing accurate customer/network data. An alarms system notifies the team of communications failures which are attended to by our engineers as quickly as possible. RTUs can store telemetry data for prolonged periods during communications outages and then transmit data once communications are reinstated. These measures ensure we have a continuous stream of accurate data with minimal gaps. On the rare instances where there are issues with the raw data received from DERMS, we employ simple data cleaning algorithms such as forward filling. RTU measurements of access reduction update on change or every 30-mins in absence of change. We also minimise postprocessing of RTU data (e.g. we do not time average data). Using the raw data allows us to ascertain event start and end times of curtailment actions exactly and accurately determine access reductions experienced by our customers. Assurance Statement The dataset is generated and updated by a script which is scheduled to run daily. The script was developed by the DSO Data Science team in conjunction with the DSO Network Access team, the DSO Operations team and the UKPN Ops Telecoms team to ensure correct interpretation of the RTU data streams. The underlying script logic has been cross-referenced with the developers and maintainers of the DERMS scheme to ensure that the data reflects how DERMS operates. The outputs of the script were independently checked by the DSO Network Access team for accuracy of the curtailment event timings and access reduction prior to first publication on the Open Data Portal (ODP). The DSO Operations team conduct an ongoing review of the data as it is updated daily to verify that the operational expectations are reflected in the data. The Data Science team have implemented automated logging which notifies the team of any issues when the script runs. This allows the Data Science to investigate and debug any errors/warnings as soon as they happen.

Other

Download dataset information: Metadata (JSON)

Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary: https://ukpowernetworks.opendatasoft.com/pages/glossary/ To view this data please register and login.

Between November 2022 and October 2023, organizations in the education sector worldwide saw around 872 instances of data breaches caused by hacking. The professional industry ranked second, with 603 data breach cases in the measured period. Furthermore, hacking caused 598 data breach incidents in the finance sector.

The datasets available for download are 2014 and 2015 permit breach statistics. These are open data. This record is for Approval for Access AfA406. Condition breaches on Regulated sites under various legislation (Environmental Permitting Regulations from 2010). This dataset excludes attribution that allows location or operator to be identified.More detailed current data is available under AfA403 ‘Compliance Classification Scheme’. Records go back to about 2004. Breaches for the following permit types: - waste operations - industrial process installations - water discharge activities - groundwater authorisations - abstraction licences - radioactive substances (RAS) permits Attribution statement: © Environment Agency copyright and/or database right 2015. All rights reserved.

Between November 2022 and October 2023, the education saw 860 data breach cases caused by system intrusion. Basic web application attacks resulted in 161 data breaches in the finance sector. Social engineering attacks caused 158 data breaches in the construction sector.

Between November 2022 and October 2023, organizations in the professional sector worldwide saw around 429 instances of data breaches caused by malware attacks. Public administration ranked second, with 292 data breach cases in the measured period. Furthermore, malware caused 165 data breach incidents in the healthcare sector.

Security breaches, leaks, and certified erasure rates for refurbished assets.

Nei giorni 5-6 novembre 2019 l'università Bicocca ha ospitato un hackathon con il tema della sostenibilità ambientale. Il sito dell'evento consigliava di utilizzare alcuni dataset contenuti nel portale Open Data Lombardia. In particolare, dato lo stretto legame con il tema dell'evento, si consigliava di utilizzare le seguenti categorie: Ambiente, Energia, Mobilità e trasporti, Sanità. Il dataset contiene la rilevazione delle statistiche sul numero di visualizzazioni e di download dei dataset del portale nei giorni del contest.